Solution Architect - AI-Led Enterprise VMware Exit to AWS Cloud

Job Description Must Have Technical/Functional Skills

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field; advanced degree preferred.
• 12+ years of technology architecture and engineering experience, including 5+ years designing enterprise AWS cloud migration, modernization, or data center exit solutions.
• Proven experience architecting large VMware or VMC exit programs covering discovery, assessment, target-state architecture, AWS foundations, migration waves, testing, cutover, stabilization, and decommissioning.
• Strong hands-on architecture experience across VMware estates, AWS landing zones, multi- \account strategy, networking, identity, security, compute, storage, databases, observability, backup, disaster recovery, and cloud operations.
• Experience defining workload disposition and migration patterns including rehost, replatform, refactor, retire, retain, and repurchase.
• Experience applying AI, automation, analytics, and agentic capabilities to accelerate infrastructure and application discovery, dependency analysis, wave planning, code and configuration assessment, testing, migration execution, and evidence generation.
• Experience designing secure, resilient, compliant, and auditable solutions for regulated enterprises; insurance or financial services experience preferred.
• AWS Certified Solutions Architect Professional, AWS Migration, VMware, networking, security, or equivalent certifications preferred.
• Deep knowledge of AWS Organizations, Control Tower, IAM, VPC, Transit Gateway, Direct Connect, Route 53, EC2, EBS, S3, RDS, container services, CloudWatch, CloudTrail, Systems Manager, Backup, and disaster recovery patterns.
• Strong understanding of VMware vSphere, vCenter, ESXi, NSX, storage, backup, monitoring, licensing, and dependency considerations relevant to exit planning.
• Expertise in migration assessment, application dependency mapping, target platform selection, workload disposition, migration wave design, cutover architecture, rollback, resilience, and decommissioning controls.
• Proficiency in infrastructure-as-code and automation using Terraform, CloudFormation, scripting, APIs, CI/CD, configuration management, and policy-as-code.
• Working knowledge of AI/ML, Generative AI, Agentic AI, LLM-based analysis, model governance, Human-in-the-Loop validation, prompt and output controls, and auditability of AI-assisted decisions.
• Strong application, infrastructure, network, data, identity, security, privacy, compliance, observability, FinOps, and operational architecture skills.
• Ability to lead architecture workshops, document decisions and trade-offs, resolve cross-domain dependencies, and communicate effectively with executives, business owners, engineers, security teams, and vendors.

Roles & Responsibilities

• Define the end-to-end target solution architecture for an enterprise VMware/VMC exit to AWS Cloud, aligned to business outcomes, security, compliance, resilience, scalability, operability, cost, and migration timelines.
• Lead current-state discovery and architecture assessment across applications, infrastructure, VMware platforms, networks, data, integrations, security controls, operational tooling, and business dependencies.
• Establish workload disposition criteria and target hosting patterns for rehost, replatform, refactor, retire, retain, and repurchase decisions, with documented ration ale, risks, dependencies, and acceptance criteria.
• Design the AWS landing zone and foundational architecture, including account structure, identity and access, network connectivity, segmentation, DNS, security services, logging, monitoring, encryption, secrets, backup, disaster recovery, and operational controls.
• Create reusable reference architectures and migration patterns for common application, database, middleware, file, batch, integration, and infrastructure scenarios.
• Define an AI-led architecture approach for discovery, dependency analysis, migration recommendations, code and configuration assessment, wave planning, test generation, evidence creation, and operational insights.
• Ensure AI-generated findings and recommendations are traceable, explainable, security-reviewed, and validated through accountable Human-in-the-Loop architecture and SME approvals before execution.
• Partner with application, infrastructure, network, security, data, automation/IaC, testing, release, compliance, FinOps, and operations teams…